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Record W3136415440 · doi:10.1130/b35829.1

Autogenic translation and counter point bar deposition in meandering rivers

2021· article· en· W3136415440 on OpenAlexaff
Zoltán Sylvester, Paul R. Durkin, Stephen M. Hubbard, David Mohrig

Bibliographic record

VenueGeological Society of America Bulletin · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of CalgaryUniversity of Manitoba
FundersDepartment of Mechanical Engineering, University of Texas at AustinUniversity of Texas at AustinU.S. Geological SurveyNational Aeronautics and Space Administration
KeywordsPoint barGeologyDeposition (geology)Meander (mathematics)Bank erosionChannel (broadcasting)GeomorphologyErosionRiparian zoneHydrology (agriculture)SedimentGeometryFluvialGeotechnical engineeringStructural basin

Abstract

fetched live from OpenAlex

Abstract Although it has long been recognized that deposition along meandering rivers is not restricted to convex banks (i.e., point bars), the consensus is that sediment deposition on concave banks of channel bends mostly occurs when meander bends translate downstream because erosion-resistant barriers inhibit their lateral migration. Using a kinematic model of channel meandering and time lapse satellite imagery from the Mamoré River in Bolivia, we show that downstream translation and associated concave bank deposition are essential, autogenic parts of the meandering process, and resulting counter point bars are expected to be present whenever perturbations such as bend cutoffs and channel reoccupations create short bends with high curvatures. The implication is that zones of concave bank deposition with lower topography, finer-grained sediment, slack water, and riparian vegetation that differs from point bars are more common than previously considered.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.200
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations53
Published2021
Admission routes1
Has abstractyes

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